When I started investing in 2013, deep learning was largely confined to research labs. Yet I was convinced back then that the most important technology companies of our generation will be built AI-first. This is because AI is a force multiplier for technological progress, and everything around us is ultimately a product of intelligence.

So in 2019, I founded Air Street to build a venture firm dedicated to this conviction.

Today, AI-first companies are emerging across every industry. We’re seeing the birth of highly capable long-horizon coding agents, AI-first vertical software products that produce human-grade work, design novel biological systems that outperform nature, control a wide range of embodied physical systems, and reshape - and test - our national security. The AI frontier has become the center of gravity of the technology industry, and by extension, every industry.

With Fund III, Air Street is now the largest solo GP venture firm in Europe. This structure enables high-conviction investing with a single decision-maker and significant capital to support the most ambitious teams building AI-first companies.

What our founders are building

Over the past decade, through my earliest angel investments and across Air Street’s funds, I’ve backed teams building across software, the physical world, science, and defense.

In software, Synthesia’s AI video platform now generates >$150M in annual recurring revenue with customers across more than 90% of the Fortune 100. Black Forest Labs‘ FLUX models have become the most widely used open visual intelligence models among developers and enterprises alike. Poolside has built a frontier AI lab operating in high-stakes enterprise and government environments.

In science, Profluent has created the first AI-designed CRISPR system (published in Nature) and demonstrated that scaling laws enable the design of ever more capable genome editors. Meanwhile, Enveda Biosciences is unlocking the chemistry of nature with AI to discover new medicines from natural products.

In the physical world, Wayve‘s AI driver operates zero-shot in over 500 cities around the world. Sereact deploys embodied AI robotics inside warehouses for leading industrial brands including BMW Group and Daimler Truck. And Lambda and Crusoe are powering the largest computing infrastructure buildout in modern history to train and deploy frontier AI.

And in defense and national security, Delian Alliance Industries is deploying autonomous defense systems to protect Europe and its allies at a time of critical need.

We have also seen several exits across Air Street funds, including Valence Discovery to Recursion Pharmaceuticals, Graphcore to SoftBank, Adept to Amazon, Allcyte to Exscientia (pre-IPO, subsequently Nasdaq-listed and merged with Recursion), Fern Labs to Poolside, and ClipDrop to Stability AI and later Jasper.

What Fund III enables

With Fund III, Air Street will lead early-stage rounds for AI-first companies in North America and Europe with checks of $500k to $15M. We will also make a small number of growth-stage investments with checks of up to $25M. Many of the teams will be well known to us, having built relationships over many years, through our network and events, and even prior investments in them.

Our focus remains on AI-first software, developer tools and infrastructure, techbio and science, and defense and security - the areas where AI is not simply improving the status quo but enabling entirely new kinds of products and companies to be built.

Building the AI community

From the beginning, Air Street has been about more than the companies we invest in.

The State of AI Report has grown into the most widely read and trusted annual analysis of progress in the field, now in ninth edition. RAAIS, our Research and Applied AI Summit, has brought together leading researchers and builders since 2017 - from the founders of DeepMind and the co-author of “Attention is All You Need” to the next generation of AI-first founders from Wayve, ElevenLabs, and Isomorphic Labs. The Air Street AI Network now connects nearly 3,000 vetted researchers, engineers, product leaders, and founders around the world from our global meetups in London, New York, San Francisco, Paris, Munich, and Zurich.

We will also remain committed to speaking out when the ecosystem needs it. Whether that is our work on spinout.fyi - where key Air Street reforms to the university spinout playbook were accepted by the UK Government - or our Air Street Press essays and policy memos, we believe that if something is worth saying, it’s worth saying publicly.

Our third epoch begins today

Fund III launches into what I believe is the most transformational period in technology of our generation. Capabilities that seemed like magic a decade ago are now real, usable, and creating enormous value. Air Street is built for exactly this moment, and I couldn’t be more energised to find and back the defining companies of this era.

I am deeply grateful for the trust and partnership of an extraordinary group of long-term partners including US university endowments, foundations, hospitals, and institutional investment platforms. Many of our existing investors significantly increased their commitments, and several are investing in a solo GP venture firm for the first time.

Air Street could not have made this progress without the hard work and ambition of our founders. It is a privilege to partner with you.

To the steadfast AI founder: come join our third epoch. We begin today.

tl;dr Air Street Capital, founded by Nathan Benaich, has raised $232M for Fund III - making it the largest solo GP venture fund in Europe. The fund invests in AI-first companies in North America and Europe across software, science, the physical world, and defense, with portfolio companies including Synthesia, Black Forest Labs, Sereact, Profluent, Delian Alliance Industries, and Poolside. Read more at www.airstreet.com and press.airstreet.com

In 2025, AI entered large-scale commercial deployment. Reasoning models made inference-time compute a new scaling axis, AI coding agents pulled automated software engineering into the present, and embodied AI alongside AI-accelerated scientific discovery emerged as major new battlegrounds. China’s open-weight ecosystem rapidly closed performance gaps with Western frontier models, even as the largest labs continued to widen their lead on capability-per-dollar. Scale remained the dominant force.

The economics followed. AI labs and public AI infrastructure companies added more net new revenue than the entire public SaaS sector. NVIDIA pushed beyond $4 trillion in market capitalization and catalyzed outsized returns across the AI semiconductor supply chain. Multi-gigawatt compute clusters moved from announcements to physical sites constrained by power availability, grid access, and geopolitics. AI infrastructure became industrial policy, with sovereign capital, export controls, and national champions shaping who can train, deploy, and profit from frontier systems. The US, in particular, moved forcefully to treat AI as a key competitive vector.

It is still early innings. Capabilities that would have seemed like magic a decade ago are now real and usable, and the opportunity ahead lies in deploying them widely and embedding them into the systems that matter. The next phase will be determined by who can convert frontier capability into durable, widespread use across economically meaningful tasks under real-world constraints of energy, economics, and politics.

In our 2025 year in review, we cover Air Street Capital, Air Street Press, the State of AI Report, and our community engagement.

Portfolio updates: AI investments across defense, healthcare, robotics, and fintech

In 2025, we made eight new investments across gaming, defense, healthcare, frontier AI and fintech, including Studio Atelico, Polar Mist, Delfa, Clove, and four companies yet to be announced. We also made two follow-on investments in techbio and defense, Profluent and Delian Alliance Industries, bringing Fund II to 18 active portfolio companies. During the year, we exited Fern Labs, a long-horizon agent company, to frontier AI company, poolside, via M&A.

Now turning to the portfolio highlights:

New investments:

Fern Labs: $3M Seed (solo investor). Fern Labs is the platform where networks of agents can create, test and iteratively improve software artifacts over long time horizons. The founding team previously built agent-driven AI systems at Palantir.

Polar Mist: Day 1 financing (co-lead investor). Polar Mist is building maritime autonomy for European defense. The company builds full-stack unmanned maritime defense, combining on-board jamming-free navigation with robust hardware platforms for long-range autonomous reconnaissance, logistics, and strike missions.

Delfa: $3.8M Seed (lead investor). Delfa is the AI-first operating system for clinical trials, starting with participant enrollment, where delays and cost are still driven by spreadsheets, phone calls, and duplicated site-by-site processes. Delfa’s agents are already deployed across 50+ live clinical trials trials.

Studio Atelico: $5M Seed (lead investor). Studio Atelico is an AI-first games studio. The founding team combines frontier ML and on-device optimization experience with award-winning game design, spanning Uber AI Labs, Meta, and Creative Assembly’s Total War franchise.

Clove: $14M Seed (co-investor). Clove is building an AI-native wealth management institution. The platform will pair regulated human advisors with an AI-first operating environment that absorbs repetitive and compliance-heavy work so advisors can serve more clients with consistent, transparent delivery.

Black Forest Labs: $300M Series B (co-investor). Black Forest Labs, founded by key inventors behind latent diffusion and Stable Diffusion, is translating research leadership into a category-defining visual intelligence company. The company has rapidly won Fortune 500 adoption and deep enterprise integrations as well as more than 400 million downloads of its open-source category-leading FLUX models.

Follow-on investments:

Sereact: €25M Series A (follow-on). Sereact is the frontier robotics research and deployment company, starting with warehouse automation. The platform supports picking and packing, quality checks, sorting, and inventory workflows.

Hedera Dx: €15M Series A (follow-on). Hedera Dx is scaling decentralized, hospital-run liquid biopsy testing using circulating tumor DNA, shifting advanced cancer diagnostics from a small number of centralized reference labs into routine clinical care across hospital networks.

Delian Alliance Industries: $14M Series A (co-lead investor). Delian builds vertically integrated, AI-first defense systems that autonomously sense and strike across land, air, and sea. Its full-stack portfolio spans GPS-denied navigation, electronic warfare, and autonomous effectors, built in Europe to defend Europe and its allies.

Profluent: $106M financing (follow-on). Profluent is a frontier AI company focused on writing biology. They’re scaling protein language models trained on the world’s largest private corpus of natural proteins (the Profluent Protein Atlas). The company has released the open source OpenCRISPR-1 AI-designed gene editor and entered into a handful of commercial deals spanning drug discovery and agriculture.

Exits:

Poolside (Fund I) acquired Fern Labs (Fund II) to deepen its push into long-running, reliable enterprise agents, bringing together agentic infrastructure and frontier models under one roof. Performance, reliability, and developer ergonomics are converging into a single stack that the winning frontier labs will own end-to-end. This deal was a special one for Air Street Capital, as we’re first investors in both poolside and Fern Labs.

Angel and pre-Air Street portfolio: AI infrastructure, enterprise software, and space

Across my angel and pre-Air Street portfolio, 2025 was a year where many AI-first companies scaled in earnest:

As the US pushed to rebuild domestic energy and AI capacity, Crusoe and Lambda both raised large rounds to meet demand for compute. Crusoe closed a $1.375B Series E at a $10B+ valuation to expand its vertically integrated AI factories, now underpinning OpenAI’s Stargate. Lambda raised a $480M Series D followed by a $1.5B+ Series E to deploy gigawatt-scale supercomputers for customers training and serving frontier models.

Elsewhere, Enveda raised a $150M Series D at a $1B valuation to advance its clinical pipeline built on AI-enabled natural compound discovery. Muon Space raised a $146M Series B to scale satellite manufacturing as demand grows for sovereign, vertically integrated space systems.

In enterprise software, Synthesia raised a $180M Series D as AI video became a default format for enterprise communication, now used by over 60% of the Fortune 100. ElevenLabs announced a $180M Series C as it closed in on $200M ARR for its AI-first audio products. PolyAI raised an $86M Series D as its voice agents handled tens of millions of high-stakes calls across healthcare, finance, and hospitality. incident.io raised a $62M Series B to build agents that actively resolve incidents with engineering teams. Motion raised $60M across Series B and C, scaling an agentic work platform used by over 10,000 businesses. Finally, Numerai raised a $30M Series C to continue running an AI-native hedge fund at scale.

Two companies were acquired in 2025: Niantic, creator of Pokémon GO, was acquired by Scopely for $3.5 billion, closing out one of the most successful stories in blending AI, games, and the physical world of all time. Ravelin, an AI-first fraud prevention platform for online commerce, was acquired by WorldPay.

Air Street Press

Air Street Press is the home for all of our analytical essays on AI research, industry playbooks as they take shape, policy memos, monthly State of AI newsletters, the annual State of AI Report, and our global event series.

In the last year, Air Street Press has clocked over half a million views, up almost 50%. Our most popular work included:

• 2025 is the year of open-endedness

• Sovereign AI is political branding

• The AI factory illusion

• AI progress, after 2025

• Is the EU AI Act actually useful?

• Can AI discover new science?

• The AI rollup mirage

State of AI Report 2025

In October, we released the eighth edition of the State of AI Report, the most widely read and trusted annual review of progress across AI research, industry, politics, and safety. The 2025 edition focused on the emergence of reasoning models, the economics of frontier systems, the resurgence of open-weight models, power as a binding constraint, and the acceleration of sovereign AI efforts among governments with the capital and political will to pursue them.

The report reached an even broader audience than in prior years and was a discussion springboard across leading AI labs, startups, governments, and investors. It was debated, critiqued, and pressure-tested in public and private, which is precisely its role.

To mark its release, we hosted State of AI launch events in San Francisco and New York, bringing together founders, researchers, and operators to discuss what the year’s progress actually implies for deployment, competition, and policy.

Those conversations continued across podcasts, panels, and closed-door sessions in the months that followed, informing how many in the field are thinking about what comes next. You can tune into podcasts I joined on TBPN, Matt Turck’s MAD podcast in New York, Turner Novak’s The Peel podcast, Daniel Bashir’s The Gradient podcast, and TechBio Talks with Chris Gibson (Recursion) in New York.

Community: AI researchers, founders, and operators

In 2025, we continued to spend time where we learn best: in rooms with people actually building and deploying AI systems. Through Air Street AI meetups and the ninth edition of the Research and Applied AI Summit (RAAIS), we brought together hundreds of researchers, founders, and operators to compare learnings on what is working, what is breaking, and what is changing faster than expected.

We keep RAAIS and Air Street AI meetups intentionally small and highly curated, with an emphasis on open discussion. Conversations in 2025 ranged from AI-first biology and coding agents to defense, open models, and the realities of scaling systems under real-world constraints. The goal is simple: help people learn faster from one another and leave with ideas they can apply immediately.

RAAIS remains a highlight of the year, which you can watch on our YouTube channel and read the takeaways on Air Street Press. All proceeds support the RAAIS Foundation, which funds open educational resources and works to broaden participation in advanced AI research. We’ll return on 12 June 2026 for the tenth edition and continue to host events across Europe and North America throughout the year. Join us in Munich (17 Feb), Zurich (19 Feb), Paris (11 March), San Francisco (29 April), and London (12 June).

Here’s to a big 2026!

If 2023 and 2024 were about discovering and pushing what AI could do, 2025 was about learning where it actually works in the real world at scale. The next chapter will be defined less by frontier breakthroughs and more by diffusion: who can make AI dependable, affordable, and embedded in the systems that matter. That is where we continue to focus, and where we are most excited to build. We can’t wait to see what the community achieves in the coming year!

As ever, drop me a reply on nathan@airstreet.com if you’re building in AI.

To the AI community, the names Robin Rombach, Andreas Blattmann, and Patrick Esser sound very familiar. In the summer of 2022, their work on Latent Diffusion and Stable Diffusion bent the arc of visual AI by ushering photorealistic image generation onto the internet. The world went nuts, hundreds of thousands of developers built with the models, and Twitter feeds could speak (or show) nothing else. Indeed, this proceeded the low key research preview of ChatGPT, which celebrated its third birthday only yesterday.

So for the trio that ignited a revolution in generative media, founding Black Forest Labs (BFL) was the natural next step. And their focus was to translate research leadership into a category‑leading visual intelligence company built for the next decade of multimodal computing.

Today, I’m excited to unveil Air Street’s investment in Black Forest Labs as it announces a landmark funding milestone: a $300M Series B, following a previously unannounced Series A. Together, these rounds represent a big step in scaling the company’s momentum, with Black Forest Labs now capitalised with half a billion dollars and trusted by leading Fortune 500 enterprises.

From breakthroughs to exceptional execution

In under 14 months, Black Forest Labs has delivered one of the most impressive execution runs we have seen in generative AI. The company has gone from zero revenue to significant scale with Fortune 500 adoption, deep enterprise integrations, and use cases ranging from e-commerce to robotics simulation. More than 400 million downloads of their open-source FLUX models make them the most widely deployed visual-generation systems in the world. FLUX has become shorthand for quality, controllability, and reliability.

The company exhibits a set of attributes that are very rare to find and are the result of sustained, disciplined ingenuity and velocity across research, product, and go‑to‑market:

• Frontier AI, open by default. Black Forest Labs defined what open frontier visual AI should look like: high‑quality, open base models with transparent research lineage and enterprise‑grade deployment options that meet the standards of global companies.

• A new standard for controllable image editing. FLUX.1 Kontext and FLUX.2 transformed image generation from a probabilistic slot machine into a precise creative instrument, effectively creating a new standard for professional image editing workflows.

• Compounding enterprise traction. From social platforms to global retailers, Black Forest Labs systems are already embedded in high‑value, high‑volume production workflows that demand reliability, governance, and predictable performance.

• A world‑class team scaling with purpose. In under a year, the team has grown from 15 to more than 40 researchers, engineers, and operators from NVIDIA, Meta, Databricks, Google, Spotify, AWS and more, forming one of the most concentrated talent clusters in visual AI.

• An enormous market. It would not be a stretch to say that billions of images are uploaded to the internet every day. And with visual AI progressing at such a pace, a large proportion of these and new content will switch to being generative from the outset.

Watch co-founder Andreas Blattmann’s talk at our 10th annual Research and Applied AI Summit in London, 13 June 2025:

The road ahead: multimodal intelligence and beyond

The roadmap reflects the Black Forest Labs’ intent to define - not simply participate in - the next frontier of visual AI. FLUX.2 launched just last week and offers impressive precision, efficiency, control and extreme realism at a fraction of the cost of systems like Google’s Nano Banana Pro. Try it on the playground!

Next up will be more powerful multimodal models that offer market-leading infrastructure to underpin how billions of people and systems create, reason, simulate, and communicate.

This shift is already visible in early customer deployments: models are moving from generating images to supporting full creative workflows, interactive video, and multimodal understanding. As the boundary between perception and generation collapses, the winners will be the companies that combine frontier research with the reliability and depth required by the world’s largest enterprises.

Black Forest Labs is that company.

Into the New

Black Forest Labs is is building the visual foundation of the next decade of AI - open, controllable, multimodal, and trusted in the most demanding settings. We’re proud to be working with Robin, Andreas, Patrick, and the entire team as they continue to define what visual intelligence can be.

🌲 Into the New 🌲

For more than three centuries, wealth in the United Kingdom has been shaped by institutions like Coutts, founded in 1692 and long associated with a model of personalised service reserved for a narrow segment of society. That heritage has defined expectations of advice as something scarce, relationship driven and costly to deliver. Today that world looks very different. The mass affluent have grown in number and complexity, accumulating meaningful assets while facing a financial landscape defined by rising volatility, shifting pensions, tightening tax regimes and the largest generational transfer of wealth in modern UK history. At the same time, the institutions that should support them have not kept pace. Advisors are scarce, legacy systems are slow and the burden of navigating important long term decisions has fallen back onto individuals.

It is against this backdrop that Air Street Capital is investing in Clove’s $14M first financing round, backing a new kind of wealth institution built for this generation rather than the last. This investment reinforces our long-term thesis that AI-native financial institutions will reshape how wealth is planned, managed and delivered.

Clove’s founders, Christian Owens and Alex Loizou, see this gap not as an inevitability but as the result of infrastructure that was never built for the modern consumer. With Clove, they are creating a new kind of financial institution, one designed from the ground up for people who want trustworthy guidance but have been priced out or ignored by traditional services. Their platform brings together regulated human advisors with an AI-first environment that handles the repetitive and compliance heavy processes which dominate advisory work today. By removing friction and expanding advisor capacity, Clove can deliver high quality personalised guidance at a scale that has not been possible before.

Rebuilding the wealth stack

The scale of the advice gap in the United Kingdom captures both the urgency and persistence of this problem. Thirteen million households hold between £50k and £5M in investable assets, yet nearly three quarters of them receive no professional guidance. Many are left to navigate pensions, taxes and long term decisions alone, despite the growing complexity of the financial landscape.

This lack of support is reflected in the way people use even the simplest tax efficient investment tools such as the ISA. Only around 40% of adults hold an ISA and fewer than 10% of ISA holders use their full £20,000 personal allowance each year. This illustrates a broader pattern in which millions of people with meaningful savings struggle to make informed choices. DIY investing platforms were designed for trading rather than long term planning and regulatory efforts aimed at transparency cannot compensate for the operational limits of institutions that still rely on outdated processes.

Clove is stepping into this gap with the goal of widening access to high quality financial guidance. Indeed, only around 8.6% of UK adults received regulated financial advice in the twelve months to May 2024, and half of advisers have stopped serving less wealthy clients. The Financial Conduct Authority estimates that 7M adults with at least £10K in cash savings may be missing out on the benefits of long term investing. Clove aims to close this structural gap by building a modern institution able to support a far broader population with consistency, trust and transparency.

Our long-term partnership

We have known Alex for more than a decade and backed him before. He and Christian bring the experience of building and scaling complex fintech and marketplace platforms. Christian founded Paddle and scaled it into a global payments infrastructure platform serving software companies around the world. Alex founded Trouva which created a novel consumer marketplace for independent retailers in Europe. Their shared background in regulated operations, financial infrastructure and global distribution positions them well for the challenge of rebuilding the wealth industry. Both founders have also experienced the gaps in the current advisory market personally which gives them strong conviction and a clear understanding of consumer needs.

A new standard for wealth advice

Air Street Capital invests in Clove as the first AI-native wealth institution designed for the mass affluent. This investment reflects our belief that category-defining companies in financial services will be AI-native from day one. We believe Clove represents a step change in how financial guidance will be delivered over the next decade. The company combines the humanity and regulatory discipline of professional advisors with the scale, consistency and analytical power of AI. This combination has the potential to expand access to high quality advice for millions of households who have been underserved for years.

As AI transforms financial planning globally, Air Street Capital is committed to backing the institutions that will define this new category. We are excited to support Christian, Alex and the Clove team as they build a new institution for the mass affluent and set a new standard for modern wealth management.

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I first reached out to Ali Madani in 2021 via a Twitter DM after featuring his team’s breakthrough work on AI‑generated proteins in that year’s State of AI Report. It caught my attention as one of the first real proofs that generative models could produce functional biological machinery that outperformed naturally occurring variants. The Salesforce Research preprint, later published in Nature Biotechnology, showed that large language models could generate de novo proteins that functioned as well as, or better than, those found in nature. It was the clearest evidence yet that biology could be written with intent rather than discovered by chance. A few months later, Ali founded Profluent, and I wrote one of the largest first checks in Air Street’s history.

Now, that early idea has become a defining company at the frontier of AI-first biology. Profluent has raised $106M led by Bezos Expeditions and Altimeter, with continued support from Air Street, Spark, and Insight. The company is now the largest position in Air Street’s second fund. This new capital, which brings the total raised to $150M, accelerates the company’s path toward scaling frontier protein models and, ultimately, delivering the first AI-designed therapeutic to a human patient.

The shift from serendipity to design

For decades, biological progress relied on chance discoveries: CRISPR stumbled upon in bacteria, antibodies evolved through slow mutagenesis, enzymes refined through years of directed evolution. In 2019, I argued that the next generation of biotech companies would break from this pattern and move toward deliberate, AI-driven design. Profluent is the strongest realisation of that shift.

In just three years, the company has built the world’s largest private corpus of natural proteins, the Profluent Protein Atlas, and demonstrated - clearly and repeatedly - that scaling laws apply to biological design. ProGen evolved into ProGen3, a frontier-scale model capable of generating functional proteins across diverse families. Larger models predict function better and produced proteins that folded, bound and acted in cells in ways nature never explored.

Breakthroughs that changed the field

The moment that crystallised Profluent’s leadership was OpenCRISPR‑1, the first AI-designed genome editor. Released openly, it was downloaded tens of thousands of times within a day, with more than a hundred licence requests from pharma, agriculture, biotech, and academic labs. For the first time, a large model had generated a genome-editing molecule from scratch.

Profluent has since expanded the frontier with E1, the first retrieval-augmented model for protein engineering - bringing techniques from natural language systems directly into biological design. And with Protein2PAM, the team made gene editors programmable with high precision by designing the exact DNA motifs they can recognise. This unlocks edits at genomic sites previously out of reach and extends the company’s capabilities into antibodies, enzymes and other high‑value modalities.

Profluent’s scientific momentum has translated into commercial traction across multiple industries. A multi‑year collaboration with Corteva Agriscience is applying frontier gene-editing tools to next‑generation crops. A strategic partnership with Revvity combines Profluent’s AI-engineered enzymes with the Pin‑point™ base‑editing platform to produce more precise adenine deaminases. And with Integrated DNA Technologies, Profluent is co‑designing enzymes that move seamlessly from in silico creation to ready‑to‑use reagents for researchers.

What this capital unlocks

This $106M round enables Profluent to scale its foundation models even further, expand its automated wet‑lab capacity, and strengthen its commercial footprint across three major verticals. Most importantly, it brings the company closer to an extraordinary milestone: dosing the first human patient with an AI‑designed therapeutic.

My conviction in Profluent has only deepened over the years of our collaboration and work to get to this inflection point. Ali and the team have shown technical originality, scientific rigour, and execution speed at a level rarely seen. Profluent is defining the shift from reading biology to writing it.

I’m proud to continue supporting them as they push programmable biology into its next, most consequential chapter.

When I first met Ash in late 2024 through a mutual friend at Palantir, Fern Labs was still operating under a codename. Despite the early state of the company, one thing was obvious: Ash had the rare combination of technical depth, velocity, and clarity of product intuition that you only see in the very best AI founders. His colleagues at Palantir described him as someone with exceptional taste, the ability to lock onto the right problems before others see them, and the person who repeatedly pushed the organisation toward generative AI long before it was fashionable. This is exactly the founder phenotype that we look for at Air Street: technical, pragmatic, and customer value-centric.

With Fern, Ash was pushing toward something more ambitious and more precise than yet another agent framework. His goal: to solve long-running, reliable agents by creating an agentic operating system that fused structured reasoning, tool use, and traceability. It echoed many of the ideas I wrote about in Open-endedness is all we’ll need, which argued that the next stage of truly usable AI agents will come from systems that continually generate novelty, self-improve, and orchestrate long-horizon tasks in unpredictable environments. Ash had independently arrived at similar conclusions long before we ever compared notes, particularly the importance of systems that can adapt, iterate, and expand their own capabilities over time.

Over a quick succession of calls and async docs, Ash explained how Fern’s architecture was built around this exact intuition: long-running loops of generation, critique, repair, memory, and tool use that allow agents to improve across episodes rather than simply complete a single task. Indeed, Fern was an early glimpse of the systems that will eventually handle complex, multi-stage real‑world workflows. Succeeding at this mission would be of critical importance to the success of agents in the enterprise.

Without hesitation, Air Street led the entire $3M financing to back Ash before the company had a website, a deck, or a team. Shortly thereafter, Alex and Taylor - two equally talented Palantirians - jumped into Fern and we were off to the races. Here’s what happened next.

From short demos to 10-hour autonomous builds

One of the cleanest ways to understand Fern’s progress is through time horizons of autonomous agent work. Around the time METR published excellent work on measuring how long current agents can run before falling apart, even the best models like Sonnet 3.7 only reliably handled tasks under an hour - a far cry from real-world workflows. Meanwhile, Fern was already pushing well beyond those limits.

In the early months, the team focused on short autonomous builds that were concrete enough to stress-test the system end to end. Fern would spin up several agents with their own virtual machines, access to tools and a product requirement document for what the user wanted Fern to create. Fern agents built products like a remote interviewing tool in roughly three hours of autonomous work, a Slack-style workspace clone with real functionality, and a functional video calling site with screen sharing, recording and no noticeable latency on live cross-continental video calls. Taken together, these showed that Fern could wire together non-trivial features across frontend, backend, and data.

By the time Ash joined us at the Air Street NYC AI meetup in mid-April 2025, Fern was routinely running agents for several hours at a time, up to 10 hours - a feat that the field had not yet demonstrated publicly. In that talk, he showed something important: when you let agents run longer, the quality of the resulting software build compounds. With more time, the same Fern agents produced more polished UIs, discovered and implemented advanced features like search, authentication, and richer workflows that you would normally only expect from a human team iterating over multiple sessions. At a moment when the best public evaluations suggested that agents struggled beyond a few hours of equivalent human work, Fern was quietly demonstrating 5x that horizon in the wild.

The next chapter

In tandem with Fern accelerating its revenue build via a forward deployed motion with large enterprises, frontier labs were expanding their agentic capabilities. It was becoming clear that winning the largest prize in AI - agents that write, maintain and improve all software - requires compounding compute, models, tools, and workflows into a unified development environment. Put another way, a frontier AI company would need Fern, just as Fern would need a frontier AI company.

Meanwhile, poolside - the frontier AI company tackling the most high-stakes deployments - had been pursuing exactly this vision. I’ve been involved with poolside since before its creation and as one of the first investors with Air Street’s first fund. I have seen firsthand how Eiso and Jason built the company from a set of clearly articulated beliefs - including that mastering software development is a viable path to AGI - into a real contender at the frontier.

Following an introduction I’d made between Eiso and Ash, the two companies began exploring what a go-to-market partnership could unlock in each other’s roadmaps. It rapidly became clear that joining forces would help both companies reach their shared roadmap far faster.

What poolside and Fern are building together

Poolside is building one of the strongest full-stack AI companies in the world by uniting the energy, compute, models, and infrastructure needed to run multi-agent systems for complex enterprise workflows under one roof. The Fern team brings a deeply opinionated agentic core - built from first principles and stress-tested on real workloads - into a company with scale, distribution, and compute firepower. Fern’s architecture was built for agent specialization and coordinated long-horizon work - the exact capabilities poolside can scale into a full production environment.

Together, they can move faster on:

• Agentic infrastructure that actually scales, not just demos well.

• Enterprise-grade traceability and safety primitives, which Fern built from the ground up.

• Developer tooling that makes reasoning systems predictable, debuggable, and production-ready.

• Integrated model and agent loops, which poolside is uniquely positioned to own end-to-end.

This is the direction the market has been moving toward: a world where model performance, agent reliability, and developer ergonomics form a single continuum. Here, the winners will own that entire stack - a belief I have held for many years.

A word about the AI community Air Street is building

One of the privileges of working in AI for over a decade is seeing how networks of startups, bigger companies, and researchers compound. The opportunity to work with Ash on Fern existed because my network surfaced an exceptional founder when he took the risk to build something new. Poolside exists because that same community has been pushing the frontier of AI capabilities for years.

When these networks work well, moments like this emerge organically: founders meet each other, visions align, and the right strategic move becomes the obvious one. Indeed, the best outcomes don’t happen by accident - they emerge when the right people, the right ideas, and the right timing align. This is one of those moments.

I’m proud to have backed both entrepreneurs early. I’m even more proud to have helped bring them together.

And I’m excited for what they will build next! 👀

Understanding how protein sequence encodes structure and function remains one of the central challenges in the life sciences. Yet most protein language models still treat each sequence as an isolated datapoint. This forces the entire burden of evolutionary context into model parameters, which leads to blind spots in underrepresented families and amplifies the biases of sequence databases. Profluent’s (an Air Street portfolio company) new E1 family demonstrates that this constraint is no longer necessary. Retrieval augmentation, a technique that transformed natural language processing, is now beginning to reshape protein modeling by allowing models to incorporate evolutionary information at the moment of inference rather than storing it all in weights.

E1 is built around this shift. Its design embraces the idea that proteins are best understood in the context of their evolutionary families. Functional motifs, co-evolving residues, and structural constraints emerge most clearly when a sequence is compared to its close relatives. By enabling the model to draw on homologous sequences during training and inference, E1 brings this relational structure directly into computation.

How E1 works

The core architectural idea behind E1 is block-causal multi-sequence attention.

Instead of examining a single protein in isolation, the model receives a query sequence preceded by a set of unaligned homologs. It alternates between layers that attend within each sequence and layers that allow residues to attend across sequences. Because these homologs are unaligned, E1 must implicitly learn how positions correspond across evolutionary space. This mirrors the flexibility that made retrieval so powerful in NLP, where models learn to use external context without relying on rigid structure.

Training further reinforces this capability. Profluent trained E1 on 4 trillion tokens drawn from its proprietary Protein Atlas and UniRef, gradually increasing both the length of multi-sequence instances and the number of homologs included. The result is a family of models at 150M, 300M, and 600M parameters, all released under a permissive license for research and commercial use.

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E1 sets the new open-source standard

To understand how this works in practice, consider a protein engineer developing next-generation CRISPR genome editors. Profluent’s earlier model, OpenCRISPR1, showed that language models can learn meaningful relationships among thousands of CRISPR-associated nucleases with minimal supervision. Now imagine an engineer seeking to design a Cas effector variant with improved specificity for a therapeutic context. With E1, they could provide the wild-type nuclease sequence, retrieve its closest evolutionary neighbours from public databases, and ask the model to score how candidate mutations might affect activity or off-target behavior. Because E1 processes the nuclease in the company of its evolutionary relatives, it can highlight conserved positions that tolerate little variation, identify residues whose variation correlates with altered PAM preferences, and surface rare substitutions found only in distant homologs that may expand targeting range.

This kind of enriched reasoning is reflected in benchmark performance. On ProteinGym, E1 establishes a new high-water mark for open-source zero-shot mutation effect prediction. At equivalent parameter scales, all three E1 variants outperform both ESM-2 and ESM C. When homologs are included at inference time, E1 surpasses PoET and MSA Pairformer, reaching an average Spearman correlation of 0.477 and an NDCG at rank ten of 0.788 for the 600M model. The advantages are clearest in protein families with limited multiple sequence alignment depth, where single-sequence models often fail and retrieval provides critical additional signal.

Structural reasoning follows the same trend. Using the Categorical Jacobian method to estimate residue-residue contacts, E1 outperforms the ESM-2 family across every scale. On CAMEO, a continuous blinded benchmark for protein structure prediction, the 600M variant achieves a long-range precision at L of 0.512 in single-sequence mode and 0.541 with retrieval. Examples in the technical report show how retrieval sharpens contact predictions in regions where single-sequence models misinterpret noise as structure.

Taken together, E1 treats evolutionary relationships not as static information distilled during pretraining but as an active force shaping inference. This allows a single model to specialise dynamically for different families without fine-tuning, avoids catastrophic forgetting, and reveals clear scaling behaviour across the released model sizes.

Advancing open protein engineering science

The open release of E1 materially shifts what is possible for research groups and startups working in protein engineering. It offers state-of-the-art performance while remaining small enough to run locally, lowering the computational barrier that often limits exploratory design work. Because the model does not require aligned MSAs, it also simplifies the early phases of design when sequences may be diverse or poorly characterised.

More broadly, E1 shows how ideas first proven in natural language modeling are now regularly diffusing into protein engineering. Retrieval-augmented inference revolutionised document and knowledge retrieval by allowing models to draw on external context at runtime rather than relying entirely on what they had memorised. Profluent has now demonstrated that the same principle applies in biology. By giving a protein model the ability to pull in relevant evolutionary context on demand, E1 represents an important step toward biological foundation models that can flexibly integrate many sources of information.

This cross pollination between AI and biology, where architectural innovations migrate from language to sequence analysis, is likely to guide the next several years of progress. Retrieval may prove to be the missing connective tissue between today’s sequence-only models and future systems that reason jointly over evolutionary, structural, and experimental evidence.

Read the technical report

For PARIMA, the approval validates years of research and development, as well as industrial scaling work. The company has built one of the largest cultivated food facilities in the world, developed novel cell lines, as well as bioprocess engineering techniques and infrastructure for scalable production. Its category-leading regulatory portfolio now spans nine active filings across major jurisdictions. Moreover, PARIMA strengthened its supply chain by acquiring a key cell line supplier in the cultivated food ecosystem, Vital Meat, to further consolidate its position as a global leader in next-generation proteins.

But make no mistake: Singapore’s approval is no accident. Since 2019, the city-state has pursued a coherent national strategy for alternative proteins within its broader “30 by 30” food security plan aimed at producing 30% of the nation’s nutritional needs locally by 2030. This includes a predictable, science-led regulatory process and targeted incentives for industrial deployment. The result is a functioning market where new food technologies don’t just pilot. They sell.

Europe, meanwhile, continues to incubate world-class science without the political will to deploy, buy or scale it. The EU’s novel foods pathway has become a regulatory maze that would challenge the labyrinths of Greek and Minoan civilization in its complexity. According to Nature Food, the average time for approval of a novel food application in the EU now exceeds 35 months - a delay that effectively sidelines startups and deters investment. Worse yet, it rewards incumbents and retards innovation. PARIMA’s approval in Singapore is not a failure of European science. It is a failure of European policy.

Cultivated food is now entering its industrial era. The question is not whether the technology works - it does - but where it will scale. If Europe wants to lead in sustainable food production, it must match scientific excellence with regulatory competence. Otherwise, the continent will remain a laboratory for breakthroughs that commercialize elsewhere. And if Europe is content to let others commercialize its discoveries, it should be honest about that reality.

Read more from Parima

The story of cultivated meat has so far been one of scientific triumph and industrial constraint. Several startups have proven that animal proteins can be grown from cells rather than animals, yet few have crossed the chasm from lab bench to factory floor. The bottleneck is less about taste and technology and is more about scale and regulations.

This month, our portfolio company Gourmey acquired Vital Meat to form PARIMA, a new European champion for cultivated proteins and foods. The merger brings together two complementary pioneers: Gourmey, known for its work in high-value duck and poultry cell lines, and Vital Meat, focused on scalable cell-based chicken production. Together, they now control the critical layers of the cultivated stack, from cell biology to bioprocess design to regulatory readiness, while moving beyond scaled R&D into genuine food manufacturing. Its Paris facility, one of the largest of its kind worldwide, anchors a new phase of industrialization: large-volume production, supply chain integration, and cost parity with high-value meats.

What’s behind the announcement

PARIMA combines Gourmey’s full-stack industrial platform, including premium cultivated duck validated by Michelin-starred chefs and independently verified production costs below €7/kg, with Vital Meat’s Nantes infrastructure, namely its 2,000‑liter bioreactor capacity and advanced avian cell lines developed from ~25 years of Groupe Grimaud’s avian cell research. The combined platform is supported by 15+ patent families and 70+ patent applications, and a regulatory portfolio of nine active filings across major regions. PARIMA’s near-term goal is to become the first cultivated food company positioned for regulatory approval of two species (duck and chicken), with applications that span premium dining to large-scale B2B ingredients.

A milestone for Europe’s bioeconomy

Just as the AI industry is integrating vertically around compute and data, cultivated food is consolidating around production infrastructure and process efficiency. The winners will not be the first to demonstrate a prototype, but the first to make a tonne.

At Air Street, we’ve backed Gourmey since its early days because we believed that cultivated food would follow the same curve as other deep technology sectors: a burst of discovery, a period of consolidation, and then a race to scale. PARIMA embodies that transition: from a scientific breakthrough to an industrial one.

While you’re here: another milestone with Mission Barns hitting retail!

Alongside PARIMA’s formation, another Air Street portfolio company reached a landmark: Mission Barns has begun selling cultivated pork products to consumers in the U.S. Following an FDA “no questions” letter for its cultivated pork fat in March 2025 and USDA clearance in July 2025, Mission Barns launched with Bay Area restaurant partner Fiorella and is executing targeted retail releases. Notably, its cultivated pork meatballs are making a supermarket debut at Berkeley Bowl in the East Bay, one of the first times cultivated meat has been sold at a U.S. grocer.

When Air Street portfolio company poolside launched in 2023, it set out to build artificial general intelligence by mastering software development in the most demanding environments. Software, the company argued, requires reasoning, planning, and an understanding of the world. The same ingredients underpin human cognition. From the outset, poolside’s mission has been to translate those principles into systems that operate reliably and at scale, supported by infrastructure built to last.

Now that conviction takes physical form. Poolside has unveiled Project Horizon, a 2GW AI campus in West Texas designed from the ground up for multi‑agent systems. Through a partnership with CoreWeave, which will anchor and operate the first 250MW phase, poolside is combining compute, power, and intelligence under one roof to create a vertically integrated foundation for the agentic enterprise.

From data centers to AI factories: a purpose-built cloud for AI agents

As we wrote in the State of AI Report 2025, power has become the new bottleneck for AI progress as large labs compete for resources to train and serve the insatiable demand for their frontier models. Multi-GW clusters have moved from slideware to site plans, and grid constraints are beginning to shape both roadmaps and margins. What’s more, AI infrastructure has become a lever of national strategy. This means that the ability to generate, store, and apply compute power independently is a question of technological sovereignty.

Project Horizon is poolside’s answer to these challenges. It is a ground-up data center development designed to give the company long-term control over power, cost, and scale through vertical integration. The campus will be developed in eight phases of 250MW each, ensuring scalable, modular growth aligned with advances in compute demand and silicon efficiency. In doing so, poolside can commit to providing long-term access to energy, compute, and intelligence to serve the most demanding of customers.

Located in West Texas, the site sits near a major natural gas hub with access to existing water, processing, and fiber infrastructure. It occupies part of the Longfellow Ranch, whose owners, the Mitchell family, have supported Horizon’s development across their expansive property. Dual long-haul fiber routes provide high-bandwidth, low-latency connections to major network hubs, while the behind‑the‑meter design allows poolside to directly manage its power generation. The campus is engineered for modular expansion, built with redundancy at every layer of the power stack. Generation will come from efficient aero‑derivative turbines meeting strict emissions standards, supplemented by grid‑interconnect redundancy and on‑site battery storage to improve reliability and load balancing.

A milestone for poolside

For poolside, Horizon is an infrastructure milestone and a major expansion into controlling the inputs that determine who reaches the frontier. In an industry where access to energy and clusters now defines the limits of ingenuity, Horizon gives poolside direct control over the two scarcest resources in AI: power and compute. Owning this foundation means the company can train and serve frontier‑scale models on its own timeline, protect customers from the volatility of cloud pricing and availability, and invest in sustained capability growth rather than short‑term capacity.

This level of vertical integration - from model and agent development through to power generation and deployment - gives poolside a durable advantage as AI systems become indispensable to enterprise and national infrastructure alike. Project Horizon turns energy and compute from external constraints into internal levers of scale, marking poolside’s growth from an AI model builder into an industrial company of intelligence.

AI is the ultimate force multiplier on technological progress in our digital, data-driven world. Our mission at Air Street Capital has always been to back the most ambitious teams building breakthrough AI products that would have seemed like magic when I started investing over a decade ago. Key to these inflection points is the infrastructure that enables them: NVIDIA computing systems. I have long argued that NVIDIA is the defining company of the AI era, powering breakthroughs in science, industry, and national strategy. This is why I’m excited to share that we are deepening that story together: Air Street Capital is partnering with NVIDIA as part of its new £2B commitment to the UK AI ecosystem.

NVIDIA announced this week that it will invest in and support UK-based AI startups with a mix of capital, compute, and technical expertise. This builds on its role as the critical enabler of the global AI industry, which we’ve showcased in depth since 2018 in our State of AI Reports, as the demand for GPUs reaches new heights from researchers, startups, enterprises, and governments alike.

This partnership reflects the conviction that Air Street Capital and NVIDIA both share: the UK has what it takes to produce generational AI-first companies, provided founders can access world-class compute infrastructure and communities. In my 10+ year career investing in AI, I’ve backed many leading UK AI companies including ElevenLabs, Synthesia, Wayve, PolyAI, V7 Labs, Fern Labs, and Thought Machine. Alongside these investments, I’ve built communities like London.AI and the Research and Applied AI Summit, where the best in AI come together to share learnings and ideas to advance research and products.

Now, with the combined backing of NVIDIA and Air Street Capital, the UK AI ecosystem is primed to enter a new phase of growth and global impact.

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That’s why we’re leading the $3.8M Seed round for Delfa. The team is building an AI-native operating system for clinical trials, starting with patient enrolment, the most broken part of the process.

The bane of spreadsheets and phone tag

There are 22,000+ drug candidates in the global pipeline but just ~5,000 new trials start each year. More than a third of the cost and delay comes down to a single operational challenge: finding, qualifying, and enrolling patients.

It’s still a manual funnel. Sites use Excel trackers. Recruiters cold-call lists, answer the same questions, collect medical histories by phone, and schedule visits by email. Every site solves the same problem in isolation.

Delfa solves recruitment with AI agents

Delfa replaces the human bottleneck with AI agents that fulfill the role of expert recruiters. They are trained with clinical trial documentation and guardrailed accordingly. Every patient interaction is logged, reviewed, and scored across 20+ QA metrics. Physicians audit the outputs. Alerts fire in real time when something looks off.

The AI agents can speak with patients in any language by phone, SMS, or chat; prescreen for medical eligibility; answer questions; collect histories; book visits; and write directly into a site’s CRM or CTMS.

In just a few months, Delfa’s solution is live across 50+ trials and multiple indications. Delfa works with top site networks like Flourish, Elevate, and Re:Cognition. Importantly, these site networks had the option of continuing with large recruitment teams and legacy tools, but chose Delfa instead. In one migraine study, a single site exceeded its enrollment target by 25%, saved 20 recruiter hours, and achieved an 11% lead-to-enrolled rate. In a vaccine trial, Delfa’s agent matched human conversion rates.

Why we invested

We’ve spent years backing the entrepreneurs building the future of drug discovery, including Valence Discovery (acq. by Recursion), Allcyte (acq. by Exscientia), and most recently, Profluent. That work gave us a front-row seat to how promising science gets bottlenecked in the clinic.

Delfa is the first team we’ve seen turn that bottleneck into software infrastructure. They’re translating the potential of AI agents into something that helps real patients, real sites, and real sponsors. But recruitment is just the beginning. Delfa is building a full-stack OS for clinical trials: from smarter site selection to automated study start-up and long-term patient retention. It’s a bet on turning clinical ops from artisanal workflows into scalable software.

We’re hiring!

AI agents are finally good enough to take on repetitive, high-volume, high-compliance workflows in healthcare. Delfa is one of the few teams translating that capability into measurable ROI, and doing it in a wedge that touches every new drug.

CEO Tom Farrand has built and deployed applied AI across J&J, Medtronic, and Exscientia. CTO Gustav Bredell trained at ETH Zurich, built early foundation models at Roche, and has deep experience in medical AI systems.

Come join the growing team in London and SF!

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You can also read more from in who has the launch story:

Upstarts Media

The Startup Speeding Up Clinical Trials With Voice AI

The Upshot…

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7 months ago · 6 likes · Alex Konrad

We’re excited to announce Air Street Capital’s lead investment in Studio Atelico’s $5M Seed round, alongside friends including Chris Ré (Stanford), Thomas Wolf (Hugging Face), and Alex Ratner (Snorkel), to back a team on a mission to redefine the role of generative AI in games.

Founded by Piero Molino, Paul Szerlip, JP Chen, and Ennio De Nucci, Studio Atelico brings together deep experience in AI model development, on-device optimisation, and award-winning game design. The founders met through years of collaboration at Geometric Intelligence (acquired by Uber) and forming Uber AI Labs together, where they built state-of-the-art ML systems including Ludwig. After Uber, Piero spent some time with Chris Ré (which is how I met him!) and then went on to found AI startup Predibase, which was acquired by NYSE-listed Rubrik in June 2025. Meanwhile, JP went to Instagram/Meta to work on large-scale on-device inference and generative AI. Over in London, Ennio led game design for Creative Assembly’s Total War series, including the Warhammer trilogy and Three Kingdoms, which sold millions of copies worldwide.

Why now?

The next era of games will be defined by generative AI. Not as a bolt-on, but as a core game mechanic. Yet today’s AI-powered games face steep hurdles: API-based inference costs eat into margins, latency interrupts immersion, and developers have little control over model behaviour.

Studio Atelico’s answer is an on-device AI engine that gives developers and itself as a studio full control. The engine powers lifelike NPCs with rich dialogue, planning, and emergent behaviours, all without server latency or per-interaction costs. It integrates directly into major game engines like Unreal, Unity, and Godot. The result: richer worlds, deeper player engagement, and the economics to scale.

We’re especially excited by Atelico’s approach to build and launch its own games on top of this technology. Gaming is a hits-driven business, and the value of a unique capability is best realised when the team uses it to make genre-defining titles. The studio’s first releases will target high-growth mobile and cross-platform markets, with gameplay experiences impossible to deliver without this AI-first foundation.

Piero captures it well in his GamesBeat interview:

“The magic happens when AI stops being a gimmick and becomes the canvas for the entire game.”

We’re hiring!

With this round, Studio Atelico will scale its engineering and design teams, ship the first public version of its AI engine, and launch its debut titles. We’re proud to back them from the start and can’t wait to play what comes next.

You can read more in the company’s funding announcement and GamesBeat coverage.

Today marks a significant scientific milestone for frontier AI systems in biology. Our portfolio company Profluent has its work on OpenCRISPR-1, the first AI-designed CRISPR-Cas protein that can precisely edit human DNA, published in the highest impact scientific journal, Nature.

This work demonstrates how generative language models can design functional, novel genome editors from scratch that outperform natural and previously engineered systems on key criteria including specificity, immunogenicity, and versatility.

A break from evolution

Genome editors like CRISPR-Cas9 have powered a revolution in biotechnology and was awarded the 2020 Nobel Prize in Chemistry. This technology has enabled treatments like the first CRISPR-based cure for sickle cell disease (Casgevy), ex vivo engineered CAR-T cell therapies for cancer, and in vivo liver editing for transthyretin amyloidosis/

However, this system carries evolutionary baggage. Engineered for bacterial immunity, SpCas9 (the most widely used Cas protein in genome editing) tolerates off-target binding, triggers immune responses in humans, and is large enough to complicate delivery, all of which are non-desirable features.

So how might we improve on this design? Learn from evolution and guide search to more optimal designs.

Profluent trained their large protein language model ProGen2 on a vast custom dataset called the CRISPR–Cas Atlas, mined from 26.2 terabases of microbial genomes. This yielded over 1.2 million CRISPR operons and more than 240,000 Cas9 sequences, offering an unparalleled training ground for AI to learn the statistical “grammar” of functional genome editors .

The power of generation

The model, fine-tuned on Cas-specific sequences, was prompted to generate 350,000 synthetic proteins, which were then filtered for quality and CRISPR compatibility. From these, 209 candidates were tested in human cells. The standout was OpenCRISPR-1, a protein 403 mutations away from SpCas9 and 180 mutations from its nearest natural cousin.

Despite this evolutionary sequence difference, it matched SpCas9’s on-target activity while reducing off-target edits by 95%!

Notably, OpenCRISPR-1’s off-target activity was a subset of SpCas9’s, with no new unintended edits. This suggests the editor is not just better, but safer, a critical threshold for therapeutic use.

Built-in therapeutic advantages

Profluent didn’t stop at editing efficiency. They assessed OpenCRISPR-1’s immunogenicity, finding that it lacks known T cell epitopes that plague SpCas9. In antibody assays across 40 donors, OpenCRISPR-1 showed consistently lower immune reactivity.

Further, it’s compatible with base editing and performs robust A-to-G conversions when fused to either evolved or AI-generated adenine deaminases. This hints at a future where entire gene editors, from protein to guide to payload, are AI-designed, component by component.

A new CRISPR era begins

OpenCRISPR-1 is not merely a new tool. It’s the first fully synthetic genome editor, born not from directed evolution or structural tinkering, but from massive-scale data-driven learning. It reflects a shift in biotech: from bioprospecting to bioengineering, from natural history to generative design.

And Profluent has open-sourced it all.

Plasmids for OpenCRISPR-1 are now live on Addgene, the central plasmid hub. Code, models, and sequences are available on GitHub and Zenodo. It is, in every sense, open science for labs, companies, and researchers around the world to build on.

Why this matters

CRISPR’s first chapter was defined by discovery. The next will be defined by design.

With OpenCRISPR-1, Profluent has proven that AI can leapfrog biology’s constraints to create entirely new proteins that are safe, specific, and clinically promising. The impact is immense: bespoke editors for rare diseases, compact versions for viral delivery, and lower immunogenicity for in vivo therapies.

AI didn’t just optimize biology, it created a new branch of it.

We’re proud to support Profluent as they lead the way into the programmable protein era.

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We are witnessing a rare convergence of strategic urgency, political momentum, and technological readiness in European defense. At a time when NATO allies are committing to invest 5 % of GDP annually in defense and Europe’s fiscal rules are being suspended via Readiness 2030 to mobilise close to €1 trillion for defense spending, one thing is clear: Europe cannot import its future security. It must build it now.

Today, I’m excited to share that Air Street has co-led a $14M Series A into Delian Alliance Industries, a company redefining European defense with vertically integrated, AI‑powered systems that autonomously sense and strike across land, air, and sea. Delian’s full‑stack product portfolio, ranging from GPS‑denied navigation to electronic warfare and autonomous effectors, is built in Europe, for Europe, breaking from the slow legacy production model.

Europe’s strategic turning point

Europe is in the midst of a strategic pivot, driven by the codification of urgency across major initiatives. NATO’s Updated Defence Production Action Plan, approved in February 2025, commits allies to accelerate industrial capacity, aggregate multinational demand, and deliver critical capabilities at pace reflecting a broader shift in doctrine. Notably, in 2024 NATO defense equipment spending more than doubled compared to 2014, signaling pressure to restock munitions, modernize systems, and streamline industrial responsiveness.

Meanwhile, the EU’s Readiness 2030 plan (previously “ReArm Europe”) is set to mobilise close to a trillion euros in defense investment via five pillars, including the €150 billion SAFE instrument for joint procurement and activation of the national escape clause under the Stability and Growth Pact to unlock fiscal flexibility. Readiness 2030 explicitly emphasizes buying together, buying better, and buying European with at least 65 % of procurement targeting equipment sourced from EU, EFTA, or Ukraine.

This rare synchronization of NATO commitments and EU mechanisms means financial and political alignment at magnitude: defense investment at 5 % GDP, a pan‑European procurement vehicle validated with €150 billion in capital, and streamlined fiscal rules that bypass traditional constraints.

Delian: The New Prime

Delian is building what we can be thought of as an Iron Dome fit for the era of autonomous warfare - a software-defined defense shield that connects any sensor to any effector, enabling autonomous detection and defeat of incoming threats across land, air, and sea. This AI-powered kill chain gives democratic nations an edge in asymmetric and autonomous warfare.

Delian represents a fundamentally different approach. Founded by Dimitrios Kottas, a former Apple Special Projects Group engineer, Delian combines the agility of Silicon Valley with the urgency of European deterrence. The company builds everything in-house: autonomous surveillance towers, edge compute comms systems, GPS-denied navigation modules, loitering munitions, and anti-UAV electronic warfare.

As Kottas puts it: “Our competitor is time. Our adversaries are developing and procuring things at speeds we cannot imagine…Our national security processes cannot support such speeds right now because they are tuned for peace time.”

This is what Europe needs to defend itself against autonomous war: rapid iteration, vertical integration, and manufacturing close to the battlefield. Just as Anduril proved in the US, Delian shows that Europe, too, can produce its own “new primes.”

From Aegean to Arctic

Delian’s roadmap aligns closely with the defense needs of middle powers, countries like the UK, Poland, Finland, Greece, Estonia, Sweden and the UAE, that sit on the fault lines of rising geopolitical tension. Their budgets can’t support Cold War-style force structures. They need smart systems that are inexpensive, scalable, and built for autonomy-first warfare: swarms, sensor networks, software-defined kill chains.

The next war won’t be won by stockpile - it will be won by systems that deploy in days, not decades.

Delian already has a growing deployment underway in a NATO country and is seeing significant international demand for its systems that are adapted to present and future-day conflicts.

What comes next

Europe must now shift decisively from defense at the speed of bureaucracy to defense at the speed of software. That means rebuilding a native defense tech ecosystem that values mission autonomy, open architectures, and rapid fielding. It means reforming export controls and removing barriers for dual-use founders. And it means investing in the talent, infrastructure, and financing mechanisms to ensure that Europe doesn’t just buy sovereignty - it builds it.

At Air Street, we believe the future of defense is sovereign, software-defined, and born on the edges of conflict. Delian is proof that it’s already underway.

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This week, Hedera Dx announced a €15M Series A round to scale its decentralized, hospital-run liquid biopsy testing model. The round was led by Vsquared Ventures, with participation from existing investors.

Rather than operating its own commercial lab, Hedera Dx enables hospitals to perform advanced blood-based cancer diagnostics in-house. The company provides a comprehensive 32-gene circulating tumor DNA (ctDNA) panel, standardized lab protocols compatible with existing next-generation sequencing (NGS) instruments, and a cloud-based software layer for variant calling and clinical interpretation.

Enabling diagnostics at the edge

Most genomic cancer testing today is centralized. Patients have their blood drawn or tissue biopsy taken in one hospital, the sample is shipped to a reference lab in another city or country, and results arrive a week or more later. This model has worked reasonably well in the U.S. with large national payors, commercial lab scale, and a handful of dominant precision oncology players. But it doesn’t translate well to Europe or to much of the rest of the world.

Hedera’s premise is simple: cancer diagnostics should be locally available, fast, and scalable. By equipping hospital labs to run liquid biopsies themselves, the company cuts turnaround time from 10–14 days down to under 48 hours. That time matters. It enables more responsive treatment decisions, faster adjustments when therapies fail, and better integration with the pace of clinical care.

The platform is already deployed in hospital labs across 11 countries, including Switzerland, Germany, France, Italy, Spain, and the Netherlands. These are public hospitals and regional cancer centers, not elite private institutions, which speaks to the practicality of Hedera’s model. This isn't about labs becoming genomics research centers. It's about giving them tools they can use without rebuilding workflows or retraining entire teams.

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Liquid biopsy as infrastructure for cancer care

Liquid biopsy is often discussed in terms of blockbuster tests: early detection screens, minimal residual disease assays, tumor profiling panels. But the companies that win long-term will be those that build durable diagnostic infrastructure—flexible enough to support new tests, easy to integrate, and hard to displace once embedded.

Hedera is aiming to become that layer. Its system is test-agnostic and already expanding beyond tumor profiling. With this funding round, the company will accelerate development of new assays for minimally residual disease and early detection, while expanding its analytics capabilities via Hedera Frame, a real-world data platform aggregating clinical and molecular insights across its customer base.

Why this model fits Europe

European hospitals are under pressure to modernize cancer care, but centralized models often conflict with national health systems, procurement structures, and data privacy regulation. Hedera’s model is well-suited to this context: it keeps data local, respects institutional autonomy, and integrates with standard lab equipment already in use.

This decentralization also has second-order benefits. It allows hospitals to maintain ownership of their clinical data, avoid vendor lock-in, and participate in shared real-world evidence networks without sacrificing control. Hedera’s software layer turns each participating lab into both a testing site and a data node, accelerating the feedback loop between practice and evidence.

From a commercial perspective, the company benefits from network effects: the more hospitals using its platform, the stronger its dataset and analytics tools become. And because it's embedded in workflows and IT systems, switching costs are high.

Real-world data generation

Hedera Dx is actively collaborating with leading cancer centers and patient organizations to generate clinicogenomic evidence through its Hedera Frame platform. This global, prospective, patient-centric data hub gathers clinical information, tumor genomics data, and resulting outcomes from patients with solid tumors who have undergone NGS-based molecular testing. By ensuring reference quality for computational methods, Hedera Frame aids the advancement of agile, real-world evidence-based clinical trials, contributing to the development of next-generation tumor biomarkers and groundbreaking therapies.

One notable initiative is the EMPATHY NSCLC study, the first prospective clinical validation and real-world utility study for the European, NGS-based decentralized liquid biopsy approach. This multicenter study enables real-world assessment of current ctDNA-based liquid biopsy practices in hospital laboratories across a dozen leading European cancer centers. It also represents the first asset of a larger, pan-cancer longitudinal clinicogenomics evidence effort aiming to collect real-life testing patterns with liquid biopsies, ultimately unleashing the potential of this highly promising technology and developing the next generation of breakthrough personalized therapies.

By harnessing the power of its clinicogenomics evidence and tapping into deep clinical expertise, the company generates unique prospective outcomes that drive advancements in precision medicine. Collaborative efforts encompass a range of offerings, including state-of-the-art software analysis and ctDNA assays, with full adherence to IVDR compliance.

To enhance patient engagement, Hedera Dx has developed the myHedera app. This application enables patients to actively participate in their care by providing Patient-Reported Outcomes and Patient-Reported Experience Measures, placing patients at the center of their own care and fostering a collaborative relationship between healthcare providers, patient groups, and individuals.

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Scaling with a €15M Series A

This Series A is about scaling commercial rollout across Europe, additional regulatory approvals, and broader clinical utility. Hedera will invest in its core product suite, expand the Hedera Frame data platform, and begin developing a pipeline of future assays designed for decentralized deployment.

There’s also an enterprise element to the next phase: deeper integration into hospital informatics, more robust compliance infrastructure, and tighter feedback between lab users and the central team to fine-tune protocols and analytics.

The broader shift

What Hedera Dx is doing reflects a broader shift in oncology: from centralized, episodic testing to distributed, continuous monitoring. Cancer care is increasingly managed over time, like a chronic disease. That shift demands diagnostics that are faster, more frequent, and more embedded in the fabric of routine care.

For that to work, hospitals need infrastructure, not just access to a lab down the road, but the ability to generate, interpret, and act on molecular data themselves. Hedera’s bet is that every modern hospital will eventually run some version of this capability in-house. Its job is to make that transition as seamless and valuable as possible.

The next decade of oncology won’t just be about better drugs. It will be about retooling the systems that deliver and care to patients.

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Eighteen months ago, Air Street led the seed round for Delian Alliance Industries, a European defense company building autonomous hardware and software for the modern battlefield.

In our essay Why We Believe in Defense, we wrote:

We unapologetically believe in the right of democracies to defend themselves. It isn’t the West’s expressions of solidarity that have supported Ukraine’s heroic resistance—it’s money, equipment, and new technologies. Democracy isn’t sustained by good vibes, and freedom doesn’t come for free.

Since then, the world has only grown more volatile. Russia’s invasion of Ukraine upended assumptions about great-power conflict. The October 2023 Hamas attacks marked the deadliest day for Jews since the Holocaust. In Asia, the scramble for deterrence in the Taiwan Strait and South China Sea grows more urgent by the month. And with America’s military shield over Europe clearly fading, the continent has no choice but to step up. Defense is no longer a procurement exercise—it’s an existential industrial strategy.

We backed Delian because we believed Europe needed new defense champions capable of building the systems that legacy defense primes couldn’t or wouldn’t. This only becomes possible when world-class technologists, military veterans, defense insiders, and manufacturing experts operate under one unified mission. That conviction has only deepened.

What modern deterrence looks like

In Ukraine, we’ve seen cheap autonomous systems overwhelm exquisite billion-dollar platforms. The cost-exchange ratio has flipped. Corvettes and frigates can now be targeted by swarms of attritable drones. GPS and comms are jammed. Airspace is contested. These are the conditions in which Delian’s platforms are designed to operate—and win.

Here’s how they’re doing it.

Layer 1: See and jam everything

Delian’s first product was LAST, a sensor tower that operates 24/7 to passively detect, classify, and geolocate threats across land and sea. It uses state-of-the-art optics, radar, and signals intelligence to build a real-time picture of the domain—without emitting a detectable signature.

To actively deny the electromagnetic spectrum, Delian built M4K15, a compact, autonomy-ready electronic warfare module. It senses, locates, and jams RF and GNSS across multiple bands. Because it’s small and low-cost, M4K15 can be mounted on unmanned platforms and deployed at scale.

Together, LAST and M4K15 enable persistent intelligence, surveillance, and reconnaissance and real-time electronic countermeasures, forming the sensing and jamming foundation of Delian’s autonomous stack.

Layer 2: GPS-denied autonomy

On today’s battlefield, GPS and radio frequencies are a liability. The electromagnetic spectrum is saturated, and satellite links are often the first to go. That’s why Delian developed OSIRIS, a plug-and-play navigation module for UAVs operating in GPS-denied environments.

OSIRIS matches live onboard imagery with preloaded satellite maps to achieve high-confidence geolocation—without emitting any RF signal. It works across different terrains, is robust to map inaccuracies, and is compatible with drones of any shape, size, or manufacturer.

In other words: autonomy that doesn't rely on the cloud, a control link, or a functioning GPS signal.

Layer 3: Autonomous strikes at scale

This week, Delian announced Interceptigon: a new series of autonomous aerial and sea strike platforms designed for attritable, one-way missions. Watch the Interceptigon in action—stealth, autonomy, and firepower:

The Interceptigon effectors are low-cost, swarming-capable, and highly survivable. They’re built for one purpose: to give defenders a decisive advantage in anti-access/area denial (A2/AD) operations, especially in geographies where proximity and terrain compress reaction time—like the Nordics, Baltics, Southeast Asia, and the Aegean.

Each Interceptigon can be covertly launched from land or seabed, autonomously navigate to a target, and deliver a kinetic or explosive payload—all while operating without GPS or human input.

Toward a European autonomous defense mosaic

When integrated via command-and-control software, Delian’s sensor towers, jamming modules, navigation systems, and strike effectors form a fast, resilient, and distributed defense network. What emerges is a machine-speed A2/AD capability that is modular, scalable, and sovereign by design.

This isn’t just a product launch. It’s a preview of how Europe can reclaim credible deterrence in an age of autonomous conflict.

Regular readers of Air Street Press will know that I’m long AI for science. Back in 2019 I wrote about 6 opportunities for AI-first biology. At the time, computer vision-powered microscopy and molecular design were the hot themes. Five years later, we have a Nobel Prize for protein structure prediction and biological datasets at scales I couldn’t have imagined during my PhD just a decade ago.

Meanwhile, generative AI continues its rapid climb up the hierarchy of human cognitive tasks. Scaling laws turned model building into a roadmap, offering predictable returns on compute and data investment. That simple fact reshaped how top labs operate. First formalized by Baidu Research in 2017 and popularized in large language models (LLMs), these laws have expanded to study of post-training and more recently for inference-time compute too.

Indeed, reasoning models like o1 and o3 are starting to make real progress on domains like coding that were once considered far out of reach.

Scaling laws in biological design

So—what about biology? Let’s narrow in on proteins, where AI has had the largest impact so far. Natural evolution is a powerful search engine, but it’s slow—millions of years slow. To accelerate protein discovery, researchers have developed protein language models (PLMs) that can predict structure and function from raw amino acid sequences. These models, inspired by LLMs like GPT-2/3, treat biology as a sequence modeling problem.

ProGen, developed by the founding team of Profluent while at Salesforce Research and published in Nature Biotechnology in 2023, was an early example. It demonstrated that autoregressive models trained on protein sequences could generate novel, functional outputs across a wide range of families. ProGen2 expanded on this—but left open a set of important questions:

• Do scaling laws hold in biology?

• How should we optimize data distributions for training ever-larger PLMs?

• Does scale improve the functional capabilities of generated proteins?

• And do post-training alignment techniques work as well here as they do in human language?

Introducing ProGen3

To address these questions, Profluent have built ProGen3. It’s a family of generative PLMs built using a sparse mixture-of-experts architecture — similar to what powers state-of-the-art language models. The models are trained on the Profluent Protein Atlas v1, a rigorously curated dataset of 3.4B full-length proteins comprising 1.1T amino acid tokens.

This represents a 12x scale-up over previous models like ProGen2, and even exceeds the dataset used for Evolutionary Scale’s ESM-3. Model sizes range from 112M to 46B parameters, trained on 1.5T tokens, enabling both full sequence generation and infilling. Unlike most models, ProGen3’s output was also validated in the lab, showing real-world functionality, not just in silico benchmarks.

Crucially, ProGen3 isn’t just benchmarked in silico. The team synthesized a wide array of AI-generated proteins and tested them in wet-lab assays. The results: model outputs exhibit high fitness and functional diversity, with larger models benefiting most from alignment with lab data.

Scaling brings gifts

Profluent’s ProGen3 shows that scaling up the computational resources to pre-train PLMs predictably improves their validation losses for proteins both near and distant to their training data. The chart below shows how this result in proteins mirrors that in natural language from years prior:

So what more does scaling PLMs bring for protein engineers?

• More valid sequences are generated, and repetition drops significantly.

• Cluster diversity increases with larger models sampling from a wider and more diverse set of protein families.

• Out-of-distribution generation improves as larger models access regions of sequence space unreachable by smaller ones.

• Infilling quality rises, suggesting that larger models develop a deeper contextual understanding of protein constraints.

• In vitro expression remains robust, even for sequences with just 30% similarity to known natural proteins.

Post-training alignment with lab data further boosts performance—for example, improving stability prediction—and larger models benefit more from alignment than smaller ones.

Next steps

ProGen3 is a compelling demonstration of how scale unlocks new capabilities in biological design. It shows that techniques developed in general-purpose AI—data curation, model scaling, sparse architectures, alignment—can directly inform progress in protein engineering.

The implications are broad: bespoke proteins for therapeutics, industrial enzymes, novel delivery vehicles, and beyond.

ProGen3 isn’t just a model, it’s proof that biology now follows its own scaling laws and we’re just starting to see what that unlocks.

America’s military shield over Europe is fading. Will Europe rise to defend itself—or learn too late that no one else will?

The last few weeks of US-Europe national security policy have been nothing short of a shocking sea change for Europeans. Back in February 2025 at the Munich Security Conference, US Vice President Vance and Defense Secretary Pete Hegseth called European leaders out for not investing in their own defense capabilities and free riding on US security guarantees. Like President Trump, they called for European nations to increase defense spending to 5% of GDP and hinted at a possible reduction in American military presence in Europe.

But then came the breaking point. After a heated White House visit, President Zelenskyy left empty-handed—no minerals deal, no US security guarantees. Days later, Europe responded with its own plan: 'ReArm Europe'. The so-called White Paper for European Defence – Readiness 2030 laid out an urgent plan to overhaul Europe’s defense capabilities, aiming for full readiness by 2030.

To achieve this, the Commission wants European member states to unlock over €800B for defense by a) asking each member to increase its spending on defense by 1.5% of GDP annually and b) by raising €150B from capital markets as long-term loans backed by the EU budget to be spent on defense. Importantly, this money is to be spent on European defense companies, not US players.

Democracy won’t defend itself with the next grocery-delivery app

Air Street made its first defense investment in the summer of 2021 with US-based Modern Intelligence, building one AI for defense, with a focus on targeting. In 2023, Air Street invested in Delian Alliance Industries, building defense for autonomous warfare. The same year, I wrote an oped for the Financial Times to argue that European policy makers, industry players and investors were not taking defense seriously enough. This wasn’t a popular opinion. But times have now changed.

European venture capital is failing to go after the hardest problems. Democracy won’t defend itself with the next grocery-delivery app. While Andreessen Horowitz’s American Dynamism project may have been mocked by some in Europe, no one has been brave enough to start an equivalent drive for a “European Dynamism”.

The missing domain

While the world fixates on drones and tanks, the sea remains the greatest blind spot in modern warfare. Given that the Ukraine-Russia war is overwhelmingly fought over land with relatively minimal naval engagements, this bias makes sense.

But the real storm is at sea. Chinese warships just staged their latest show of force in the Taiwan Strait. The South China Sea is a powder keg. The Baltic Sea is a theatre for indiscriminate undersea cable cutting and gas pipeline sabotage. Europe can’t afford to ignore maritime deterrence any longer. Autonomy is just as critical at sea, yet few defense companies are stepping up to the challenge.

Investing in Polar Mist

As European leaders scramble to rebuild their defense capabilities, the most critical—and overlooked—domain is at sea. That’s why we’re backing Polar Mist—Europe’s answer to maritime autonomy.

Founded 2 years ago in Sweden by graduates of Lund University’s Engineering Physics program and the Swedish Armed Forces’ Amphibious Regiment, Polar Mist is building European maritime supremacy.

To start, the company has developed a breakthrough in maritime navigation - an optical, GPS-free positioning system that doesn’t drift over time. With traditional satellite-based systems being jammable, independent navigation is a must-have to build autonomous systems. This means surface vessels that can autonomously navigate over large distances, in all conditions, day or night, to complete a mission or reach a target of interest without satellite communication.

With this breakthrough, Polar Mist isn’t stopping at navigation. They’re building the future of unmanned maritime defense—full-stack autonomy, from on-board intelligence to fast and robust hardware platforms.

Born in Sweden, the company is starting at home—no surprise, given Sweden’s historic military shift. In 2024, it became NATO’s 32nd member, ending 200 years of neutrality since the Napoleonic Wars. Prime Minister Kristersson has recently committed to boosting defense spending to 3.5% of GDP and expanding the armed forces to 115,000 by 2030—the country’s largest military buildup since the Cold War.

Polar Mist is securing the seas so Europe can defend itself—because no one else will.

Agents for open-endedness

At Air Street, we’ve long been enthusiastic about the potential of open-ended systems to tackle economically valuable tasks. The stunning results OpenAI displayed with o1 and o3, and now DeepSeek’s R1, combined with the plummeting cost of inference suggests that AI will be used to tackle increasingly complex and ambitious tasks.

While agentic AI applications can display stellar results in the lab, they usually have a patchier record when they’re deployed on real-world tasks. Especially if you’ve not up-skilled as a model whisperer.

As we have seen, scaling test-time compute can lead to markedly better performance for LLMs. The key to success will lie in providing agents with safe, flexible environments where they can collaborate at scale, without detaching them from the actual challenges they need to solve.

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We’ve led a $3M day one financing for Fern Labs

I’m happy to share that Air Street has led a $3M day one financing in Fern Labs.

Fern Labs is building a platform to scale coordinated agent compute against hard problems. By deploying networks of agents, Fern today can create and test software, perform small data science and analysis projects, and generate and refine resources like documents and presentations. As Fern and the base models improve, the scope of solvable tasks increases, with the end goal of Fern performing large-scale projects and companies.

You can see Fern in action here, building its own version of Asana for an agentic world. Not only does Fern write the front and back ends, the site is deployable and usable by both humans and agents alike.

Branching out from Palantir to Fern

The founding team - Ash, Alex, Taylor - worked on some of Palantir’s first agent-driven AI systems. Thanks to the company’s forward deployment model, they also know what it takes to navigate the complexities of real-world implementation. In the space of just a few months, they’ve moved from an initial blueprint to their first partnerships with financial and research institutions, and fast growing companies. This is what we love to see: technical brilliance mixed with level-headed pragmatism in solving customer problems at breakneck speed.

I’m proud to have Air Street support them as they begin on their journey.

You can read more about their work on the Fern blog here and over at Fortune from Jeremy Kahn.

The team is growing - if you’re interested in the sharp end of agentic AI implementation and work on infrastructure (both cloud and model training) and large scale systems engineering, check out open roles here.

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